2 citations · 6 across the 36 of their papers we have counts for
37 papers
Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Driving
Mengshi Qi, Xiaoyang Bi, Xianlin Zhang +1
Self-supervised depth estimation is challenging for safe autonomous driving under various adverse weather conditions due to sensor perception degradation. These challenges arise fr…
A DVDrive Approach for doScenes Instructed Driving Challenge
Zijian Fu, Xiangyang Chu, Mengshi Qi +3
Instruction-conditioned trajectory prediction is an emerging problem in autonomous driving, where a model predicts the future ego trajectory not only from visual scene context and…
Question-Aware Evidence Ledgers for Video Relational Reasoning
Yilin Ou, Mengshi Qi, Huadong Ma
The VRR-QA challenge evaluates visual relational reasoning in videos, where answers often depend on implicit spatial relations, event boundaries, target identity, and dialogue cont…
SGFormer++: Semantic Graph Transformer for Incremental 3D Scene Graph Generation
Mengshi Qi, Changsheng Lv, Zijian Fu +2
In this paper, we propose SGFormer++, a novel Semantic Graph Transformer for 3D scene graph generation (SGG), which aims to parse point cloud scenes into semantic structural graphs…
The 1st PortraitCraft Challenge: A CVPR 2026 Workshop Competition on Portrait Composition Understanding and Generation
Zijie Lou, Youyun Tang, Xiaochao Qu +40
This paper presents an overview of the inaugural PortraitCraft Challenge, held as one of the official competitions at CVPR 2026. The challenge focuses on portrait composition under…
Leveraging Metric Depth for Relative Depth Prediction
Xiaoyang Bi, Shuaikun Liu, Zhaohong Liu +5
We present our solution to the 2025 SoccerNet Monocular Depth Estimation Competition Challenge. Predicting the relative depth in football scenarios is challenging, especially with…
Claude Code-Driving Scenario Mining for the Argoverse 2 Challenge
Wei Deng, Caoshengzhe Xue, Shuaikun Liu +3
We present our submission to the CVPR 2026 Argoverse 2 Scenario Mining Challenge. Our system uses a four-stage pipeline: (1) autonomous code generation via a Claude Code agent powe…
Global-Local Monte Carlo Tree Search in Vision-Language Models for Text-to-3D Indoor Scene Generation
Mengshi Qi, Wei Deng, Xianlin Zhang +1
Large Vision-Language Models have achieved significant reasoning performance in various tasks. However, there are few studies on text-to-3D indoor scene generation with LVLMs. The…
Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning
Xinyan Zhu, Yaoqi Liu, Yue Gao +3
Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support. With the rapid developmen…
VidPrism: Heterogeneous Mixture of Experts for Image-to-Video Transfer
Rui Lin, Chuanming Wang, Huadong Ma
With the rapid development of pre-training technologies, adapting large-scale Vision-Language Models (VLMs) for video understanding \emph{\ie} image-to-video transfer learning has…
NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results
Xin Li, Jiachao Gong, Xijun Wang +75
This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-…
Explainable Action Form Assessment by Exploiting Multimodal Chain-of-Thoughts Reasoning
Mengshi Qi, Yeteng Wu, Wulian Yun +2
Evaluating whether human action is standard or not and providing reasonable feedback to improve action standardization is very crucial but challenging in real-world scenarios. Howe…
RoboDriveVLM: A Novel Benchmark and Baseline towards Robust Vision-Language Models for Autonomous Driving
Dacheng Liao, Mengshi Qi, Peng Shu +4
Current Vision-Language Model (VLM)-based end-to-end autonomous driving systems often leverage large language models to generate driving decisions directly based on their understan…
Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering
Zijian Fu, Changsheng Lv, Xianlin Zhang +2
In this paper, we propose a novel Multi-Modal Scene Graph with Kolmogorov-Arnold Expert Network for Audio-Visual Question Answering (SHRIKE). The task aims to mimic human reasoning…
Synergistic Tensor and Pipeline Parallelism
Mengshi Qi, Jiaxuan Peng, Jie Zhang +3
In the machine learning system, the hybrid model parallelism combining tensor parallelism (TP) and pipeline parallelism (PP) has become the dominant solution for distributed traini…
DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency
Mengshi Qi, Pengfei Zhu, Xiangtai Li +4
Given a single labeled example, in-context segmentation aims to segment corresponding objects. This setting, known as one-shot segmentation in few-shot learning, explores the segme…
Chain-of-Evidence Multimodal Reasoning for Few-shot Temporal Action Localization
Mengshi Qi, Hongwei Ji, Wulian Yun +2
Traditional temporal action localization (TAL) methods rely on large amounts of detailed annotated data, whereas few-shot TAL reduces this dependence by using only a few training s…
Global-Local Tree Search in VLMs for 3D Indoor Scene Generation
Wei Deng, Mengshi Qi, Huadong Ma
Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable success across various fields. However, there are few studies on 3D indoor scene generation with VLMs.…
XR-VLM: Cross-Relationship Modeling with Multi-part Prompts and Visual Features for Fine-Grained Recognition
Chuanming Wang, Henming Mao, Huanhuan Zhang +2
Vision-Language Models (VLMs) have demonstrated impressive performance on various visual tasks, yet they still require adaptation on downstream tasks to achieve optimal performance…
Robust Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning
Mengshi Qi, Changsheng Lv, Huadong Ma
In this paper, we propose a new Robust Disentangled Counterfactual Learning (RDCL) approach for physical audiovisual commonsense reasoning. The task aims to infer objects' physics…
Towards Robust Unsupervised Attention Prediction in Autonomous Driving
Mengshi Qi, Xiaoyang Bi, Pengfei Zhu +1
Robustly predicting attention regions of interest for self-driving systems is crucial for driving safety but presents significant challenges due to the labor-intensive nature of ob…
A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human Pose Estimation
Wulian Yun, Mengshi Qi, Fei Peng +1
Conventional 2D human pose estimation methods typically require extensive labeled annotations, which are both labor-intensive and expensive. In contrast, semi-supervised 2D human p…
Human Grasp Generation for Rigid and Deformable Objects with Decomposed VQ-VAE
Mengshi Qi, Zhe Zhao, Huadong Ma
Generating realistic human grasps is crucial yet challenging for object manipulation in computer graphics and robotics. Current methods often struggle to generate detailed and real…
Towards Balanced Multi-Modal Learning in 3D Human Pose Estimation
Mengshi Qi, Jiaxuan Peng, Xianlin Zhang +1
3D human pose estimation (3D HPE) has emerged as a prominent research topic, particularly in the realm of RGB-based methods. However, the use of RGB images is often limited by issu…